Advanced CAE Methods for NVH Development of High-Speed Electric Axle

Mehrgou, Mehdi; Pohn, Julian; Graf, Bernhard; Priestner, Christoph; Madinabeitia, Inigo Garcia de; Deiml, Mathias

doi:10.4271/2020-01-1501

Cited by 5 publications

(1 citation statement)

References 3 publications

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“…The experiment results further validated that the estimated error was less than 4%. In [6], the influence of temperature on the modal frequency and damping ratio of the stator and winding is studied. The results show that there is more than six times difference in damping ratio at different temperatures.…”

Section: Introductionmentioning

confidence: 99%

Simulation, Verification and Optimization Design of Electromagnetic Vibration and Noise of Permanent Magnet Synchronous Motor for Vehicle

Zhang

Meng

et al. 2022

Energies

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Aiming at the electromagnetic vibration and noise problem of an 8-pole 48-slot permanent magnet synchronous motor for a vehicle, the multi-physics coupling simulation model of the motor is introduced to optimize the rotor structure of the motor to reduce the vibration and noise of the permanent magnet synchronous motor. The effectiveness of the research method is verified by the bench test in the anechoic chamber. Finite element software was used to establish the stator core and system model considering the anisotropy of materials, and the simulation model was verified by modal experiment. For the 2in1 electric drive system, the electromagnetic-structure-acoustic multi-physics coupling noise prediction model is established. Based on the three-dimensional distributed electromagnetic force excitation, the electromagnetic radiation noise of the motor under full load acceleration is calculated, and the characteristics of electromagnetic noise are analyzed. The accuracy of the electromagnetic-structure-acoustic multi-physics coupling model of permanent magnet synchronous motor is verified by the bench test results of the anechoic chamber. By changing the angle and shape of the motor rotor, the cogging torque ripple between the stator and the rotor is reduced, and the 48th order harmonic amplitude is reduced. Finally, the optimized sample is tested on the vehicle, and the 48th order electromagnetic noise can be reduced by 5–15 dB(A). The accuracy of the electromagnetic-structure-acoustic multi-physics coupling model of permanent magnet synchronous motor is verified by the bench test results of the anechoic chamber. Therefore, the research results can be further used for the design and development of a vehicle permanent magnet synchronous motor and the research on the mechanism of electromagnetic vibration and noise.

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Section: Introductionmentioning

confidence: 99%

Simulation, Verification and Optimization Design of Electromagnetic Vibration and Noise of Permanent Magnet Synchronous Motor for Vehicle

Zhang

Meng

et al. 2022

Energies

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Analysis of Dynamics and NVH of Electric Drive Unit Using Frequency- and Time-Domain Solutions

Bauer,

Haslinger,

Zinkiewicz

et al. 2023

Mechanisms and Machine Science

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Optimization of electromagnetic layout for E-machine in electric vehicles: Achieving high performance, efficiency and NVH

Ahmed,

Merino,

Mehrgou

2024

JAE

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The development of electric vehicles (EVs) presents the challenge of optimising electric machines to enhance efficiency, compactness, noise, vibration, harshness (NVH), and affordability, while reducing the dependence on heavy rare-earth materials to reduce the CO2 footprint of electric powertrains. This paper introduces a comprehensive optimization approach for the electromagnetic design of permanent magnet synchronous machines (PMSMs), employing a combination of Design of Experiment (DoE) and Robust Neural Networks (RNN). The optimization framework is utilised to comprehensively address the multiple objectives of an electric machine, including efficiency, noise, vibration, harshness (NVH), and cost. The integration of Artificial Intelligence (AI)-driven modelling has resulted in significant performance improvements, achieving up to 96% total efficiency over the entire load cycle with substantial NVH reductions of up to 20 dB, while reducing the magnet rare earth materials by 35% compared to the baseline. Furthermore, this methodology reduces the simulation time by up to 90%, demonstrating the potential of combining neural network optimization with conventional finite element simulation techniques. The validation of the AI-driven optimization approach with the measurement of the baseline and optimised electric machines for efficiency and vibration is demonstrated for correlation.

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Advanced CAE Methods for NVH Development of High-Speed Electric Axle

Cited by 5 publications

References 3 publications

Simulation, Verification and Optimization Design of Electromagnetic Vibration and Noise of Permanent Magnet Synchronous Motor for Vehicle

Simulation, Verification and Optimization Design of Electromagnetic Vibration and Noise of Permanent Magnet Synchronous Motor for Vehicle

Analysis of Dynamics and NVH of Electric Drive Unit Using Frequency- and Time-Domain Solutions

Optimization of electromagnetic layout for E-machine in electric vehicles: Achieving high performance, efficiency and NVH

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